BODA KALYAN SINGH's repositories
census_income
Performed Exploratory Data Analysis on Census Income data set using matplotlib, pandas and seaborn, Implemented Model building with Logistic Regression, Decision Tree and Random forest Achieved highest accuracy of 83.99% using Random Forest Classifier
Covid-19-Detection-using-Chest-X-Ray
•Developed a Convolution Neural Network (CNN) model from scratch for detecting Covid-19 using chest X-Ray images •Achieved highest accuracy of 92.24% and 96.66% on training and testing set post data augmentation techniques
Customer_churn
Problem Statement: You are the data scientist at a telecom company named “Neo” whose customers are churning out to its competitors. You have to analyze the data of your company and find insights and stop your customers from churning out to other telecom companies
Flight_Prediction_project
Implemented a flight prediction system using two algorithms, Linear Regression and Decision Trees, to forecast flight prices accurately. The project involved preprocessing and analyzing flight data to extract relevant features such as departure time, destination, and airline. Utilized the Linear Regression algorithm
Heart-disease-prediction-using-decisiontree
Developed a predictive model for heart disease using Decision Tree Algorithm to provide early diagnosis, performed Exploratory Data Analysis to find out the exact affecting symptom , visualized the data set using Matplolib, seaborn libraries For improving performance of Decision tree model, Got the accuracy of 84.6% by performing depth up to 15
insurance_charges_predictions
predictions of insurance charges for a particular data set using linear regression model, got an accuracy of 83.7%
recommendation-engine-for-netflixdata
Worked on Netflix movies data set which consist of 4499 movies, implemented SVD to make a prediction model to predict the best review movie to suggest customers, Performed steps like Data Preprocessing, Cleaning Texts, Training and Classification, Analysis Conclusion Created a bench mark to give the best review movie
spam-classification
Worked on SMS Spam Collection dataset to build a prediction model that will accurately classify which texts are spam or harm, Performed steps like Preprocessing dataset, NLP based Cleaning Texts and Analysis Conclusion Best accuracy is accomplished with Naïve Bayes model which characterizes 98% of the texts accurately